Overview

Questions

  1. How does synchrony of tree growth vary across regional/elevation gradients? Prediction: higher synchrony in lower latitude and lower elevation (drier) populations (SIASH, dendrochronological principles)

  2. Within populations is intraspecific synchrony greater than interspecific synchrony? Between specific species pairs? With increasing distance? Prediction: higher synchrony among intraspecific pairs, potentially higher synchrony between pine pairs versus pine-fir pairs (successional stages), and lower synchrony with increasing distance

  3. How has synchrony changed through time? Are changes more dramatic in certain populations? Is increased or decreased synchrony associated with certain environmental variables? Prediction: synchrony has increased with time, increased synchrony associated with drier, more variable time windows, changes are more pronounced in xeric populations.

Synchrony is explored here through residual correlations betweeen trees from multivariate models of tree growth.

Q2 Intra-interspecific competition

Pairwise pearson correlation summaires:

We calculated the correlation between series of tree ring growth for all unique pairs of individuals within competitive neighborhoods.

Correlation response model:

We wanted to test the relationship between the pairwise correlations and distance, size ratios, species-pairs, and locations of all of the trees. We built hierarchical mixed models with the pairwise correlations as a responses (normal truncated response (-1,1)).

## pearson_r | trunc(lb = -1.001, ub = 1.001) ~ dist + sizeratio + (1 | pair/Region:hilo) + (1 | Region:hilo/Neighborhood.x)

Q3 Synchrony through time

Pairwise pearson correlations through time:

We calculated pearson correlations between pairs of individuals within neighborhoods for 30 year time blocks with a 10 year lag to measure synchrony through time. Trends were analyzed for data summarized for species-pairs at the neighborhood level. Note this is measuring within population synchrony (in contrast to Shestakova spatial synchrony).

Spline model:

I wanted to explore fitting a spline model to look at nonlinear changes in synchrony over time for different species-pairs and at different sites.

## pearson_r ~ pair + s(decade, by = Site.x)

Climate response model:

Model selection supported SPEI as the best predictor for synchrony. I included both mean and sd in one model although the variables are correlatedd (~.3). I did not do multiple regression because of multicollinearity.

## pearson_r | trunc(lb = -1.001, ub = 1.001) ~ spei12_mean + spei12_sd + (spei12_mean + spei12_sd | Region/hilo) + (1 | pair/Region/hilo)